Overview

Dataset statistics

Number of variables13
Number of observations48035
Missing cells166344
Missing cells (%)26.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.8 MiB
Average record size in memory104.0 B

Variable types

Numeric9
DateTime1
Categorical2
Unsupported1

Warnings

Exit_Reason has constant value "Abandoned" Constant
QueuedTime is highly correlated with WaitTime and 1 other fieldsHigh correlation
RingTime is highly correlated with WaitTime vs QueuedTimeHigh correlation
WaitTime is highly correlated with QueuedTime and 1 other fieldsHigh correlation
Queue + Ring is highly correlated with QueuedTime and 1 other fieldsHigh correlation
WaitTime vs QueuedTime is highly correlated with RingTimeHigh correlation
channel is highly correlated with Exit_ReasonHigh correlation
Exit_Reason is highly correlated with channelHigh correlation
Party_Name has 48035 (100.0%) missing values Missing
TalkTime has 35313 (73.5%) missing values Missing
HoldTime has 35426 (73.8%) missing values Missing
WrapTime has 47570 (99.0%) missing values Missing
QueuedTime is highly skewed (γ1 = 28.7180407) Skewed
TalkTime is highly skewed (γ1 = 37.77623424) Skewed
HoldTime is highly skewed (γ1 = 66.00560691) Skewed
WaitTime is highly skewed (γ1 = 28.86204012) Skewed
Queue + Ring is highly skewed (γ1 = 28.86204012) Skewed
df_index has unique values Unique
Party_Name is an unsupported type, check if it needs cleaning or further analysis Unsupported
QueuedTime has 2023 (4.2%) zeros Zeros
RingTime has 37344 (77.7%) zeros Zeros
TalkTime has 12596 (26.2%) zeros Zeros
HoldTime has 12519 (26.1%) zeros Zeros
WaitTime has 564 (1.2%) zeros Zeros
Queue + Ring has 564 (1.2%) zeros Zeros
WaitTime vs QueuedTime has 37344 (77.7%) zeros Zeros

Reproduction

Analysis started2021-02-26 15:35:05.545510
Analysis finished2021-02-26 15:35:17.293859
Duration11.75 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct48035
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean248385.8969
Minimum0
Maximum613885
Zeros1
Zeros (%)< 0.1%
Memory size375.4 KiB
2021-02-26T16:35:17.369703image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile32684.7
Q1125577.5
median185058
Q3392455.5
95-th percentile564082.9
Maximum613885
Range613885
Interquartile range (IQR)266878

Descriptive statistics

Standard deviation168480.0231
Coefficient of variation (CV)0.6782994736
Kurtosis-0.8728868399
Mean248385.8969
Median Absolute Deviation (MAD)102614
Skewness0.6137574687
Sum1.193121656 × 1010
Variance2.838551819 × 1010
MonotocityStrictly increasing
2021-02-26T16:35:17.646427image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
3593551
 
< 0.1%
1377181
 
< 0.1%
3741711
 
< 0.1%
1582001
 
< 0.1%
2278341
 
< 0.1%
6127261
 
< 0.1%
4101091
 
< 0.1%
1691841
 
< 0.1%
1520631
 
< 0.1%
Other values (48025)48025
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
ValueCountFrequency (%)
6138851
< 0.1%
6138401
< 0.1%
6138391
< 0.1%
6138221
< 0.1%
6138081
< 0.1%
Distinct47720
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Memory size375.4 KiB
Minimum2020-01-01 08:22:41.999998
Maximum2021-02-21 12:20:36.000004
2021-02-26T16:35:17.784394image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:35:17.917468image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Exit_Reason
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size375.4 KiB
Abandoned
48035 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters432315
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAbandoned
2nd rowAbandoned
3rd rowAbandoned
4th rowAbandoned
5th rowAbandoned
ValueCountFrequency (%)
Abandoned48035
100.0%
2021-02-26T16:35:18.153316image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-26T16:35:18.216408image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
abandoned48035
100.0%

Most occurring characters

ValueCountFrequency (%)
n96070
22.2%
d96070
22.2%
A48035
11.1%
b48035
11.1%
a48035
11.1%
o48035
11.1%
e48035
11.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter384280
88.9%
Uppercase Letter48035
 
11.1%

Most frequent character per category

ValueCountFrequency (%)
n96070
25.0%
d96070
25.0%
b48035
12.5%
a48035
12.5%
o48035
12.5%
e48035
12.5%
ValueCountFrequency (%)
A48035
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin432315
100.0%

Most frequent character per script

ValueCountFrequency (%)
n96070
22.2%
d96070
22.2%
A48035
11.1%
b48035
11.1%
a48035
11.1%
o48035
11.1%
e48035
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII432315
100.0%

Most frequent character per block

ValueCountFrequency (%)
n96070
22.2%
d96070
22.2%
A48035
11.1%
b48035
11.1%
a48035
11.1%
o48035
11.1%
e48035
11.1%

Party_Name
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing48035
Missing (%)100.0%
Memory size375.4 KiB

channel
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size375.4 KiB
SEO
39438 
PPC
8597 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters144105
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSEO
2nd rowSEO
3rd rowSEO
4th rowSEO
5th rowSEO
ValueCountFrequency (%)
SEO39438
82.1%
PPC8597
 
17.9%
2021-02-26T16:35:18.387947image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-26T16:35:18.452222image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
seo39438
82.1%
ppc8597
 
17.9%

Most occurring characters

ValueCountFrequency (%)
S39438
27.4%
E39438
27.4%
O39438
27.4%
P17194
11.9%
C8597
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter144105
100.0%

Most frequent character per category

ValueCountFrequency (%)
S39438
27.4%
E39438
27.4%
O39438
27.4%
P17194
11.9%
C8597
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
Latin144105
100.0%

Most frequent character per script

ValueCountFrequency (%)
S39438
27.4%
E39438
27.4%
O39438
27.4%
P17194
11.9%
C8597
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII144105
100.0%

Most frequent character per block

ValueCountFrequency (%)
S39438
27.4%
E39438
27.4%
O39438
27.4%
P17194
11.9%
C8597
 
6.0%

QueuedTime
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct922
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.81804934
Minimum0
Maximum14829
Zeros2023
Zeros (%)4.2%
Memory size375.4 KiB
2021-02-26T16:35:18.534800image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median26
Q383
95-th percentile276
Maximum14829
Range14829
Interquartile range (IQR)78

Descriptive statistics

Standard deviation206.6660565
Coefficient of variation (CV)2.799668352
Kurtosis1545.004505
Mean73.81804934
Median Absolute Deviation (MAD)23
Skewness28.7180407
Sum3545850
Variance42710.85893
MonotocityNot monotonic
2021-02-26T16:35:18.668176image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43557
 
7.4%
32761
 
5.7%
02023
 
4.2%
21870
 
3.9%
11687
 
3.5%
51580
 
3.3%
7932
 
1.9%
6902
 
1.9%
8858
 
1.8%
9688
 
1.4%
Other values (912)31177
64.9%
ValueCountFrequency (%)
02023
4.2%
11687
3.5%
21870
3.9%
32761
5.7%
43557
7.4%
ValueCountFrequency (%)
148291
< 0.1%
146891
< 0.1%
124581
< 0.1%
87181
< 0.1%
62961
< 0.1%

RingTime
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct30
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.429187051
Minimum0
Maximum48
Zeros37344
Zeros (%)77.7%
Memory size375.4 KiB
2021-02-26T16:35:18.796986image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile10
Maximum48
Range48
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.817586402
Coefficient of variation (CV)2.671159383
Kurtosis12.89926706
Mean1.429187051
Median Absolute Deviation (MAD)0
Skewness3.480064307
Sum68651
Variance14.57396594
MonotocityNot monotonic
2021-02-26T16:35:18.914902image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
037344
77.7%
11683
 
3.5%
21508
 
3.1%
31346
 
2.8%
41079
 
2.2%
5766
 
1.6%
6604
 
1.3%
7473
 
1.0%
20436
 
0.9%
8413
 
0.9%
Other values (20)2383
 
5.0%
ValueCountFrequency (%)
037344
77.7%
11683
 
3.5%
21508
 
3.1%
31346
 
2.8%
41079
 
2.2%
ValueCountFrequency (%)
481
< 0.1%
431
< 0.1%
341
< 0.1%
271
< 0.1%
261
< 0.1%

TalkTime
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct69
Distinct (%)0.5%
Missing35313
Missing (%)73.5%
Infinite0
Infinite (%)0.0%
Mean0.5918094639
Minimum0
Maximum677
Zeros12596
Zeros (%)26.2%
Memory size375.4 KiB
2021-02-26T16:35:19.040946image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum677
Range677
Interquartile range (IQR)0

Descriptive statistics

Standard deviation10.28238686
Coefficient of variation (CV)17.37448872
Kurtosis1949.968285
Mean0.5918094639
Median Absolute Deviation (MAD)0
Skewness37.77623424
Sum7529
Variance105.7274795
MonotocityNot monotonic
2021-02-26T16:35:19.169905image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
012596
 
26.2%
326
 
< 0.1%
186
 
< 0.1%
196
 
< 0.1%
205
 
< 0.1%
244
 
< 0.1%
294
 
< 0.1%
274
 
< 0.1%
264
 
< 0.1%
343
 
< 0.1%
Other values (59)84
 
0.2%
(Missing)35313
73.5%
ValueCountFrequency (%)
012596
26.2%
13
 
< 0.1%
61
 
< 0.1%
81
 
< 0.1%
111
 
< 0.1%
ValueCountFrequency (%)
6771
< 0.1%
4311
< 0.1%
3641
< 0.1%
2921
< 0.1%
2191
< 0.1%

HoldTime
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct20
Distinct (%)0.2%
Missing35426
Missing (%)73.8%
Infinite0
Infinite (%)0.0%
Mean0.06733285748
Minimum0
Maximum215
Zeros12519
Zeros (%)26.1%
Memory size375.4 KiB
2021-02-26T16:35:19.292938image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum215
Range215
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.444495435
Coefficient of variation (CV)36.30464422
Kurtosis5158.649742
Mean0.06733285748
Median Absolute Deviation (MAD)0
Skewness66.00560691
Sum849
Variance5.975557932
MonotocityNot monotonic
2021-02-26T16:35:19.389449image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
012519
 
26.1%
142
 
0.1%
214
 
< 0.1%
46
 
< 0.1%
35
 
< 0.1%
103
 
< 0.1%
63
 
< 0.1%
52
 
< 0.1%
92
 
< 0.1%
112
 
< 0.1%
Other values (10)11
 
< 0.1%
(Missing)35426
73.8%
ValueCountFrequency (%)
012519
26.1%
142
 
0.1%
214
 
< 0.1%
35
 
< 0.1%
46
 
< 0.1%
ValueCountFrequency (%)
2151
< 0.1%
941
< 0.1%
911
< 0.1%
771
< 0.1%
481
< 0.1%

WrapTime
Real number (ℝ≥0)

MISSING

Distinct150
Distinct (%)32.3%
Missing47570
Missing (%)99.0%
Infinite0
Infinite (%)0.0%
Mean64.57204301
Minimum0
Maximum981
Zeros72
Zeros (%)0.1%
Memory size375.4 KiB
2021-02-26T16:35:19.506787image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median12
Q360
95-th percentile321.8
Maximum981
Range981
Interquartile range (IQR)55

Descriptive statistics

Standard deviation123.676945
Coefficient of variation (CV)1.915332692
Kurtosis12.68983522
Mean64.57204301
Median Absolute Deviation (MAD)10
Skewness3.158296191
Sum30026
Variance15295.98672
MonotocityNot monotonic
2021-02-26T16:35:19.780898image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
072
 
0.1%
1025
 
0.1%
1221
 
< 0.1%
718
 
< 0.1%
818
 
< 0.1%
515
 
< 0.1%
414
 
< 0.1%
1114
 
< 0.1%
314
 
< 0.1%
1414
 
< 0.1%
Other values (140)240
 
0.5%
(Missing)47570
99.0%
ValueCountFrequency (%)
072
0.1%
11
 
< 0.1%
210
 
< 0.1%
314
 
< 0.1%
414
 
< 0.1%
ValueCountFrequency (%)
9811
< 0.1%
7921
< 0.1%
6661
< 0.1%
6171
< 0.1%
5861
< 0.1%

WaitTime
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct922
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.24723639
Minimum0
Maximum14829
Zeros564
Zeros (%)1.2%
Memory size375.4 KiB
2021-02-26T16:35:19.914162image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q18
median27
Q383
95-th percentile276.3
Maximum14829
Range14829
Interquartile range (IQR)75

Descriptive statistics

Standard deviation206.2781788
Coefficient of variation (CV)2.741338934
Kurtosis1555.881676
Mean75.24723639
Median Absolute Deviation (MAD)22
Skewness28.86204012
Sum3614501
Variance42550.68705
MonotocityNot monotonic
2021-02-26T16:35:20.049975image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52030
 
4.2%
41934
 
4.0%
61625
 
3.4%
71579
 
3.3%
31572
 
3.3%
81505
 
3.1%
91150
 
2.4%
21020
 
2.1%
101012
 
2.1%
11918
 
1.9%
Other values (912)33690
70.1%
ValueCountFrequency (%)
0564
 
1.2%
1726
 
1.5%
21020
2.1%
31572
3.3%
41934
4.0%
ValueCountFrequency (%)
148291
< 0.1%
146891
< 0.1%
124581
< 0.1%
87181
< 0.1%
62961
< 0.1%

Queue + Ring
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct922
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.24723639
Minimum0
Maximum14829
Zeros564
Zeros (%)1.2%
Memory size375.4 KiB
2021-02-26T16:35:20.192415image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q18
median27
Q383
95-th percentile276.3
Maximum14829
Range14829
Interquartile range (IQR)75

Descriptive statistics

Standard deviation206.2781788
Coefficient of variation (CV)2.741338934
Kurtosis1555.881676
Mean75.24723639
Median Absolute Deviation (MAD)22
Skewness28.86204012
Sum3614501
Variance42550.68705
MonotocityNot monotonic
2021-02-26T16:35:20.320552image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52030
 
4.2%
41934
 
4.0%
61625
 
3.4%
71579
 
3.3%
31572
 
3.3%
81505
 
3.1%
91150
 
2.4%
21020
 
2.1%
101012
 
2.1%
11918
 
1.9%
Other values (912)33690
70.1%
ValueCountFrequency (%)
0564
 
1.2%
1726
 
1.5%
21020
2.1%
31572
3.3%
41934
4.0%
ValueCountFrequency (%)
148291
< 0.1%
146891
< 0.1%
124581
< 0.1%
87181
< 0.1%
62961
< 0.1%

WaitTime vs QueuedTime
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct30
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.429187051
Minimum0
Maximum48
Zeros37344
Zeros (%)77.7%
Memory size375.4 KiB
2021-02-26T16:35:20.443759image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile10
Maximum48
Range48
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.817586402
Coefficient of variation (CV)2.671159383
Kurtosis12.89926706
Mean1.429187051
Median Absolute Deviation (MAD)0
Skewness3.480064307
Sum68651
Variance14.57396594
MonotocityNot monotonic
2021-02-26T16:35:20.561832image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
037344
77.7%
11683
 
3.5%
21508
 
3.1%
31346
 
2.8%
41079
 
2.2%
5766
 
1.6%
6604
 
1.3%
7473
 
1.0%
20436
 
0.9%
8413
 
0.9%
Other values (20)2383
 
5.0%
ValueCountFrequency (%)
037344
77.7%
11683
 
3.5%
21508
 
3.1%
31346
 
2.8%
41079
 
2.2%
ValueCountFrequency (%)
481
< 0.1%
431
< 0.1%
341
< 0.1%
271
< 0.1%
261
< 0.1%

Interactions

2021-02-26T16:35:07.514129image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:35:07.644456image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:35:07.761869image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:35:07.888302image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:35:08.014113image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:35:08.129488image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:35:08.257966image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:35:08.383092image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-02-26T16:35:09.596920image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:35:09.724967image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-02-26T16:35:10.108705image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-02-26T16:35:11.050529image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-02-26T16:35:11.411325image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:35:11.529032image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-02-26T16:35:11.975260image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:35:12.087712image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-02-26T16:35:16.336747image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-02-26T16:35:20.675050image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-02-26T16:35:20.853397image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-02-26T16:35:21.037300image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-02-26T16:35:21.219981image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-02-26T16:35:21.383620image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-02-26T16:35:16.563770image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-26T16:35:16.853932image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-02-26T16:35:17.077020image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-02-26T16:35:17.176528image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexCall_StartExit_ReasonParty_NamechannelQueuedTimeRingTimeTalkTimeHoldTimeWrapTimeWaitTimeQueue + RingWaitTime vs QueuedTime
002020-01-01 08:22:41.999998AbandonedNaNSEO110NaNNaNNaN11110
112020-01-01 08:54:11.999998AbandonedNaNSEO1150NaNNaNNaN1151150
222020-01-01 10:41:51.999997AbandonedNaNSEO250NaNNaNNaN25250
332020-01-01 10:47:42.999996AbandonedNaNSEO600NaNNaNNaN60600
442020-01-01 11:06:46.000000AbandonedNaNSEO200NaNNaNNaN20200
552020-01-01 11:25:49.000002AbandonedNaNSEO70NaNNaNNaN770
662020-01-01 15:08:38.000002AbandonedNaNSEO230NaNNaNNaN23230
772020-01-01 16:38:39.000005AbandonedNaNSEO1190NaNNaNNaN1191190
882020-01-01 16:53:30.999999AbandonedNaNSEO120NaNNaNNaN12120
992020-01-01 16:58:58.999999AbandonedNaNSEO200NaNNaNNaN20200

Last rows

df_indexCall_StartExit_ReasonParty_NamechannelQueuedTimeRingTimeTalkTimeHoldTimeWrapTimeWaitTimeQueue + RingWaitTime vs QueuedTime
480256137372021-02-20 15:55:19.000004AbandonedNaNSEO450NaNNaNNaN45450
480266137462021-02-20 15:59:57.999999AbandonedNaNSEO70NaNNaNNaN770
480276137542021-02-20 16:11:08.000004AbandonedNaNSEO70NaNNaNNaN770
480286137642021-02-20 16:21:58.000000AbandonedNaNSEO40NaNNaNNaN440
480296137742021-02-20 16:29:51.999999AbandonedNaNSEO70NaNNaNNaN770
480306138082021-02-21 09:28:53.999996AbandonedNaNSEO600NaNNaNNaN60600
480316138222021-02-21 10:16:28.000004AbandonedNaNSEO150NaNNaNNaN15150
480326138392021-02-21 10:50:54.000001AbandonedNaNSEO1660NaNNaNNaN1661660
480336138402021-02-21 10:51:53.000003AbandonedNaNSEO1650NaNNaNNaN1651650
480346138852021-02-21 12:20:36.000004AbandonedNaNSEO80NaNNaNNaN880